Quantitative nucleic acid analysis is extensively used in biological research and clinical analysis. Some of the applications which make use of this technology include: measurement of gene expression, monitoring of biological responses to stimuli, genomic-level gene quantitation, and pathogen detection. Typically, these methodologies utilize Polymerase Chain Reaction (PCR) as a means for selectively amplifying nucleic acid sequences in a manner that allows for their detection.
While it is generally desirable to automate the quantitation process, conventional methodologies often require a degree of user input in the form of subjective interpretation and/or approximation. For example, many reference samples may need to be run to determine a standard curve. The standard curve is then used to determine quantities of unknown samples. In some other applications, reference assays and reference samples are used to provide a reference point or as training sets for relative quantitation, such as in CNV and genotyping. As a result, these techniques may suffer from reduced accuracy and significant user-induced variability. As most optimized assays are specific with high PCR efficiencies, some assays are limited by the template sequence or the primer design. The difference in PCR efficiencies and Ct0 (Ct value at one unit of template concentration) between reference and test assays reduces quantitation accuracy with conventional methods. Furthermore, in high-throughput applications where many samples are to be processed simultaneously, it is desirable to provide increased automation capabilities to improve the speed with which the analysis may be conducted.
The aforementioned limitations of conventional techniques illustrate the need for an improved method for analyzing data generated by PCR-based quantitation techniques that may increase the potential for automation while improving the quantitative accuracy, simplicity, and reproducibility of the analysis.